## [1] "There are 5 NA in the matrix X in FortStJoh station"
## [1] "Results for crop: Barley"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -810.77 -210.13 -45.77 216.09 876.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2747.565 132.603 20.720 5.49e-15 ***
## Month_1 3.671 3.271 1.122 0.27511
## Month_2 -1.625 5.889 -0.276 0.78550
## Month_3 -10.737 6.665 -1.611 0.12288
## Month_4 -21.387 5.908 -3.620 0.00171 **
## Month_5 27.866 8.546 3.261 0.00391 **
## Month_6 -5.543 4.113 -1.348 0.19282
## Month_7 -16.804 9.772 -1.720 0.10094
## Month_8 -15.634 20.745 -0.754 0.45987
## Month_9 -34.634 18.477 -1.874 0.07555 .
## Month_10 -46.651 33.615 -1.388 0.18047
## Month_11 10.350 8.267 1.252 0.22500
## Month_12 0.548 4.713 0.116 0.90859
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 442.6 on 20 degrees of freedom
## Multiple R-squared: 0.6662, Adjusted R-squared: 0.466
## F-statistic: 3.327 on 12 and 20 DF, p-value: 0.008594
## [1] "Results for crop: Canola"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -495.78 -201.82 -22.08 224.12 494.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.640e+03 1.049e+02 15.627 1.13e-12 ***
## Month_1 8.420e-01 2.588e+00 0.325 0.74832
## Month_2 -3.685e-01 4.660e+00 -0.079 0.93776
## Month_3 -2.422e+00 5.274e+00 -0.459 0.65100
## Month_4 3.546e-01 4.674e+00 0.076 0.94028
## Month_5 2.033e+01 6.762e+00 3.007 0.00697 **
## Month_6 4.823e-03 3.254e+00 0.001 0.99883
## Month_7 1.363e+01 7.732e+00 1.763 0.09316 .
## Month_8 -3.024e+01 1.641e+01 -1.842 0.08030 .
## Month_9 -9.163e+00 1.462e+01 -0.627 0.53789
## Month_10 9.810e+00 2.660e+01 0.369 0.71614
## Month_11 2.620e+00 6.541e+00 0.401 0.69294
## Month_12 2.417e+00 3.729e+00 0.648 0.52432
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 350.2 on 20 degrees of freedom
## Multiple R-squared: 0.5118, Adjusted R-squared: 0.2189
## F-statistic: 1.748 on 12 and 20 DF, p-value: 0.1302
## [1] "Results for crop: Oats"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -686.30 -298.04 14.58 346.99 723.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2594.6436 152.9915 16.959 2.45e-13 ***
## Month_1 6.9024 3.7741 1.829 0.08236 .
## Month_2 -6.6119 6.7945 -0.973 0.34211
## Month_3 0.7560 7.6902 0.098 0.92267
## Month_4 -13.0867 6.8161 -1.920 0.06924 .
## Month_5 30.1494 9.8599 3.058 0.00621 **
## Month_6 2.8667 4.7455 0.604 0.55257
## Month_7 -13.1842 11.2744 -1.169 0.25599
## Month_8 -20.3265 23.9352 -0.849 0.40581
## Month_9 -8.4361 21.3175 -0.396 0.69649
## Month_10 -45.3332 38.7836 -1.169 0.25620
## Month_11 0.5282 9.5375 0.055 0.95638
## Month_12 6.9356 5.4377 1.275 0.21676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 510.6 on 20 degrees of freedom
## Multiple R-squared: 0.5482, Adjusted R-squared: 0.2772
## F-statistic: 2.023 on 12 and 20 DF, p-value: 0.07897
## [1] "Results for crop: Peas, dry"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -587.52 -153.37 4.09 172.14 555.40
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2283.3846 106.2762 21.485 2.74e-15 ***
## Month_1 0.4053 2.6217 0.155 0.8787
## Month_2 4.8273 4.7198 1.023 0.3186
## Month_3 7.8679 5.3420 1.473 0.1564
## Month_4 -9.8101 4.7348 -2.072 0.0514 .
## Month_5 9.9292 6.8493 1.450 0.1627
## Month_6 -2.0050 3.2965 -0.608 0.5499
## Month_7 -18.6597 7.8318 -2.383 0.0272 *
## Month_8 -27.2808 16.6267 -1.641 0.1165
## Month_9 -18.9174 14.8083 -1.277 0.2161
## Month_10 22.9643 26.9412 0.852 0.4041
## Month_11 5.6301 6.6252 0.850 0.4055
## Month_12 -1.0693 3.7773 -0.283 0.7800
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 354.7 on 20 degrees of freedom
## Multiple R-squared: 0.632, Adjusted R-squared: 0.4112
## F-statistic: 2.863 on 12 and 20 DF, p-value: 0.01824
## [1] "Results for crop: Rye, fall remaining"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -332.65 -101.24 -16.46 128.25 327.91
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2918.466 69.585 41.941 < 2e-16 ***
## Month_1 -5.645 1.717 -3.288 0.00367 **
## Month_2 -11.845 3.090 -3.833 0.00104 **
## Month_3 -8.053 3.498 -2.302 0.03219 *
## Month_4 -2.919 3.100 -0.942 0.35765
## Month_5 1.866 4.485 0.416 0.68170
## Month_6 -4.689 2.158 -2.172 0.04202 *
## Month_7 6.813 5.128 1.329 0.19895
## Month_8 -12.300 10.886 -1.130 0.27190
## Month_9 2.846 9.696 0.294 0.77216
## Month_10 -21.121 17.640 -1.197 0.24516
## Month_11 10.945 4.338 2.523 0.02021 *
## Month_12 1.048 2.473 0.424 0.67638
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 232.3 on 20 degrees of freedom
## Multiple R-squared: 0.8199, Adjusted R-squared: 0.7118
## F-statistic: 7.588 on 12 and 20 DF, p-value: 4.424e-05
## [1] "Results for crop: Wheat, spring"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -956.43 -258.19 -44.64 364.59 696.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2734.4157 165.1893 16.553 3.86e-13 ***
## Month_1 -0.1194 4.0750 -0.029 0.97692
## Month_2 -5.4679 7.3362 -0.745 0.46474
## Month_3 6.0417 8.3033 0.728 0.47528
## Month_4 -22.9612 7.3595 -3.120 0.00540 **
## Month_5 30.8880 10.6461 2.901 0.00883 **
## Month_6 -1.6546 5.1238 -0.323 0.75011
## Month_7 -29.6905 12.1732 -2.439 0.02417 *
## Month_8 -5.4537 25.8436 -0.211 0.83500
## Month_9 12.9619 23.0171 0.563 0.57960
## Month_10 -42.4457 41.8757 -1.014 0.32287
## Month_11 -12.2600 10.2979 -1.191 0.24777
## Month_12 3.6215 5.8713 0.617 0.54431
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 551.4 on 20 degrees of freedom
## Multiple R-squared: 0.6203, Adjusted R-squared: 0.3925
## F-statistic: 2.723 on 12 and 20 DF, p-value: 0.02309
## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.173 -28.423 -2.651 29.108 75.982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.3923 6.4844 39.077 <2e-16 ***
## Month_1 -1.0241 0.7347 -1.394 0.1696
## Month_2 -2.0828 0.8270 -2.518 0.0151 *
## Month_3 0.4237 0.6457 0.656 0.5147
## Month_4 0.3979 0.7355 0.541 0.5910
## Month_5 0.8744 0.4300 2.033 0.0474 *
## Month_6 0.3296 0.4143 0.796 0.4300
## Month_7 0.6135 0.7436 0.825 0.4133
## Month_8 3.4547 2.1860 1.580 0.1205
## Month_9 2.2764 2.2905 0.994 0.3252
## Month_10 0.8146 2.4062 0.339 0.7364
## Month_11 0.8313 1.0375 0.801 0.4268
## Month_12 0.2330 0.7185 0.324 0.7472
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 39.01 on 49 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.3678, Adjusted R-squared: 0.213
## F-statistic: 2.376 on 12 and 49 DF, p-value: 0.0167
## [1] "There are 6 NA in the matrix X in Abbotsford station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.591 -25.158 2.764 20.422 78.130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 238.0161 6.2802 37.900 <2e-16 ***
## Month_1 0.2120 0.6940 0.306 0.7612
## Month_2 -0.9618 0.7972 -1.206 0.2333
## Month_3 1.1482 0.6190 1.855 0.0695 .
## Month_4 0.2776 0.4761 0.583 0.5625
## Month_5 0.6459 0.3066 2.106 0.0402 *
## Month_6 0.5502 0.2503 2.198 0.0326 *
## Month_7 0.5819 0.3767 1.545 0.1287
## Month_8 1.4800 0.8984 1.647 0.1058
## Month_9 2.1554 1.2924 1.668 0.1016
## Month_10 -2.0377 2.2403 -0.910 0.3674
## Month_11 -0.1693 1.5896 -0.107 0.9156
## Month_12 0.6542 0.8210 0.797 0.4293
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 37.57 on 50 degrees of freedom
## Multiple R-squared: 0.4017, Adjusted R-squared: 0.2581
## F-statistic: 2.798 on 12 and 50 DF, p-value: 0.00537
## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.414 -10.065 -0.746 10.162 41.305
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.15763 2.97528 -0.725 0.472
## Month_1 -0.21288 0.33713 -0.631 0.531
## Month_2 -0.14369 0.37948 -0.379 0.707
## Month_3 -0.10422 0.29626 -0.352 0.726
## Month_4 0.25659 0.33749 0.760 0.451
## Month_5 0.05314 0.19732 0.269 0.789
## Month_6 0.01764 0.19008 0.093 0.926
## Month_7 0.22220 0.34117 0.651 0.518
## Month_8 -1.37895 1.00303 -1.375 0.175
## Month_9 -0.70133 1.05095 -0.667 0.508
## Month_10 0.24395 1.10405 0.221 0.826
## Month_11 0.31184 0.47602 0.655 0.515
## Month_12 0.29870 0.32969 0.906 0.369
##
## Residual standard error: 17.9 on 49 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1383, Adjusted R-squared: -0.07268
## F-statistic: 0.6556 on 12 and 49 DF, p-value: 0.784
## [1] "There are 7 NA in the matrix X in Kelowna station"
##
## Call:
## lm(formula = y ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.448 -7.620 -0.728 8.497 40.598
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.95467 2.99008 -0.654 0.516
## Month_1 -0.15930 0.33505 -0.475 0.637
## Month_2 -0.04336 0.36974 -0.117 0.907
## Month_3 0.02127 0.30023 0.071 0.944
## Month_4 0.12031 0.34145 0.352 0.726
## Month_5 -0.08646 0.20025 -0.432 0.668
## Month_6 0.04892 0.18545 0.264 0.793
## Month_7 0.16642 0.34576 0.481 0.633
## Month_8 -1.33634 1.02896 -1.299 0.200
## Month_9 -0.69091 1.04250 -0.663 0.511
## Month_10 0.05866 1.08950 0.054 0.957
## Month_11 0.18527 0.46411 0.399 0.692
## Month_12 0.23704 0.32482 0.730 0.469
##
## Residual standard error: 17.35 on 47 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.09791, Adjusted R-squared: -0.1324
## F-statistic: 0.4251 on 12 and 47 DF, p-value: 0.9456
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -66.805 -32.650 -4.853 31.271 107.397
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 256.3213 5.5035 46.574 <2e-16 ***
## Predictor -1.2670 0.6753 -1.876 0.0654 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.76 on 61 degrees of freedom
## Multiple R-squared: 0.05456, Adjusted R-squared: 0.03907
## F-statistic: 3.521 on 1 and 61 DF, p-value: 0.0654
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -81.970 -31.565 -5.315 34.240 106.594
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 252.4300 5.6819 44.427 < 2e-16 ***
## Predictor -1.9819 0.7303 -2.714 0.00864 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 41.54 on 61 degrees of freedom
## Multiple R-squared: 0.1077, Adjusted R-squared: 0.09309
## F-statistic: 7.364 on 1 and 61 DF, p-value: 0.008637
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.647 -32.060 1.217 31.758 111.414
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.3407 5.5563 46.495 <2e-16 ***
## Predictor 0.1299 0.6231 0.208 0.836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.96 on 61 degrees of freedom
## Multiple R-squared: 0.0007119, Adjusted R-squared: -0.01567
## F-statistic: 0.04346 on 1 and 61 DF, p-value: 0.8356
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.125 -33.301 4.284 30.903 98.031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.4956 5.5654 46.627 <2e-16 ***
## Predictor 0.8320 0.7418 1.122 0.266
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.53 on 61 degrees of freedom
## Multiple R-squared: 0.02021, Adjusted R-squared: 0.004144
## F-statistic: 1.258 on 1 and 61 DF, p-value: 0.2664
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.396 -35.001 1.673 35.634 83.519
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.8012 5.4512 46.925 <2e-16 ***
## Predictor 0.9700 0.4313 2.249 0.0281 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.26 on 61 degrees of freedom
## Multiple R-squared: 0.07657, Adjusted R-squared: 0.06143
## F-statistic: 5.058 on 1 and 61 DF, p-value: 0.02813
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -83.556 -29.442 0.235 27.432 108.720
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.2837 5.3297 48.462 <2e-16 ***
## Predictor 0.7456 0.3357 2.221 0.0301 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.3 on 61 degrees of freedom
## Multiple R-squared: 0.0748, Adjusted R-squared: 0.05963
## F-statistic: 4.932 on 1 and 61 DF, p-value: 0.03009
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -93.47 -29.47 4.48 31.00 110.20
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 257.4176 5.4499 47.233 <2e-16 ***
## Predictor 1.0818 0.6433 1.681 0.0978 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.99 on 61 degrees of freedom
## Multiple R-squared: 0.0443, Adjusted R-squared: 0.02863
## F-statistic: 2.827 on 1 and 61 DF, p-value: 0.09778
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -82.046 -32.733 5.984 26.482 90.138
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 261.533 5.610 46.620 <2e-16 ***
## Predictor 4.142 2.137 1.939 0.0572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.68 on 61 degrees of freedom
## Multiple R-squared: 0.05804, Adjusted R-squared: 0.0426
## F-statistic: 3.759 on 1 and 61 DF, p-value: 0.05716
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -78.43 -32.16 2.58 31.56 110.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 259.492 5.901 43.973 <2e-16 ***
## Predictor 1.219 2.375 0.513 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.88 on 61 degrees of freedom
## Multiple R-squared: 0.004298, Adjusted R-squared: -0.01202
## F-statistic: 0.2633 on 1 and 61 DF, p-value: 0.6097
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.271 -31.428 0.683 29.028 113.409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.326 6.155 41.484 <2e-16 ***
## Predictor -2.654 2.384 -1.113 0.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.54 on 61 degrees of freedom
## Multiple R-squared: 0.01991, Adjusted R-squared: 0.003839
## F-statistic: 1.239 on 1 and 61 DF, p-value: 0.27
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -75.894 -33.908 4.637 32.147 113.396
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.8379 5.5353 46.761 <2e-16 ***
## Predictor 1.4081 0.9497 1.483 0.143
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.55 on 60 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.03534, Adjusted R-squared: 0.01926
## F-statistic: 2.198 on 1 and 60 DF, p-value: 0.1434
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -74.048 -32.027 1.627 31.474 110.242
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 258.4799 5.5688 46.416 <2e-16 ***
## Predictor 0.0652 0.7908 0.082 0.935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.97 on 61 degrees of freedom
## Multiple R-squared: 0.0001114, Adjusted R-squared: -0.01628
## F-statistic: 0.006797 on 1 and 61 DF, p-value: 0.9346
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.225 -7.144 -0.725 8.355 43.760
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3438 2.1256 -0.162 0.872
## Predictor -0.1347 0.2604 -0.517 0.607
##
## Residual standard error: 16.32 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.004518, Adjusted R-squared: -0.01235
## F-statistic: 0.2678 on 1 and 59 DF, p-value: 0.6067
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.267 -6.634 0.202 7.564 45.013
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.7947 2.2509 -0.353 0.725
## Predictor -0.2197 0.2866 -0.767 0.446
##
## Residual standard error: 16.28 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.009863, Adjusted R-squared: -0.006919
## F-statistic: 0.5877 on 1 and 59 DF, p-value: 0.4464
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.053 -7.076 -0.468 7.661 43.762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.06525 2.09766 -0.031 0.975
## Predictor -0.10831 0.23333 -0.464 0.644
##
## Residual standard error: 16.33 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.003639, Adjusted R-squared: -0.01325
## F-statistic: 0.2155 on 1 and 59 DF, p-value: 0.6442
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.165 -6.767 -0.976 8.876 44.489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01599 2.13357 0.007 0.994
## Predictor 0.11011 0.28919 0.381 0.705
##
## Residual standard error: 16.34 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.002451, Adjusted R-squared: -0.01446
## F-statistic: 0.145 on 1 and 59 DF, p-value: 0.7048
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.920 -6.577 -1.282 8.549 43.534
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.03528 2.13393 0.017 0.987
## Predictor -0.07384 0.17494 -0.422 0.675
##
## Residual standard error: 16.34 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00301, Adjusted R-squared: -0.01389
## F-statistic: 0.1781 on 1 and 59 DF, p-value: 0.6745
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.145 -6.495 -0.900 8.014 44.608
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.16879 2.08624 -0.081 0.936
## Predictor 0.09148 0.12955 0.706 0.483
##
## Residual standard error: 16.29 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.008381, Adjusted R-squared: -0.008426
## F-statistic: 0.4987 on 1 and 59 DF, p-value: 0.4829
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.690 -7.461 -0.860 8.699 44.493
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2249 2.0989 -0.107 0.915
## Predictor 0.1123 0.2489 0.451 0.653
##
## Residual standard error: 16.33 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00344, Adjusted R-squared: -0.01345
## F-statistic: 0.2037 on 1 and 59 DF, p-value: 0.6534
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -46.387 -7.871 -0.618 8.411 40.488
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2774 2.1858 -0.584 0.561
## Predictor -1.2829 0.8431 -1.522 0.133
##
## Residual standard error: 16.05 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.03776, Adjusted R-squared: 0.02145
## F-statistic: 2.315 on 1 and 59 DF, p-value: 0.1334
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.851 -7.678 0.699 7.647 44.001
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2263 2.2283 -0.550 0.584
## Predictor -1.1594 0.8948 -1.296 0.200
##
## Residual standard error: 16.13 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.02766, Adjusted R-squared: 0.01118
## F-statistic: 1.679 on 1 and 59 DF, p-value: 0.2002
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.833 -6.907 -1.584 8.155 44.254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.04718 2.35924 -0.020 0.984
## Predictor 0.07959 0.89926 0.089 0.930
##
## Residual standard error: 16.36 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.0001328, Adjusted R-squared: -0.01681
## F-statistic: 0.007834 on 1 and 59 DF, p-value: 0.9298
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.555 -6.350 -1.233 8.015 43.287
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2708 2.1088 -0.128 0.898
## Predictor 0.3270 0.3591 0.910 0.366
##
## Residual standard error: 16.33 on 58 degrees of freedom
## (3 observations deleted due to missingness)
## Multiple R-squared: 0.01409, Adjusted R-squared: -0.00291
## F-statistic: 0.8288 on 1 and 58 DF, p-value: 0.3664
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.438 -6.220 -0.707 8.187 44.789
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05742 2.08668 0.028 0.978
## Predictor 0.29648 0.29543 1.004 0.320
##
## Residual standard error: 16.22 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01678, Adjusted R-squared: 0.000119
## F-statistic: 1.007 on 1 and 59 DF, p-value: 0.3197
## [1] "There are 7 NA in the matrix X in Kelowna station"
## [[1]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.992 -9.255 -1.606 11.268 43.006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4578 2.2363 -0.205 0.838
## Predictor -0.2100 0.2744 -0.765 0.447
##
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared: 0.009509, Adjusted R-squared: -0.006729
## F-statistic: 0.5856 on 1 and 61 DF, p-value: 0.4471
##
##
## [[2]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.275 -10.258 -0.622 10.410 45.131
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.2857 2.3564 -0.546 0.587
## Predictor -0.3889 0.3029 -1.284 0.204
##
## Residual standard error: 17.23 on 61 degrees of freedom
## Multiple R-squared: 0.02631, Adjusted R-squared: 0.01035
## F-statistic: 1.648 on 1 and 61 DF, p-value: 0.204
##
##
## [[3]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.713 -9.686 -0.877 10.913 42.753
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05739 2.19069 0.026 0.979
## Predictor -0.23172 0.24567 -0.943 0.349
##
## Residual standard error: 17.33 on 61 degrees of freedom
## Multiple R-squared: 0.01438, Adjusted R-squared: -0.001783
## F-statistic: 0.8897 on 1 and 61 DF, p-value: 0.3493
##
##
## [[4]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.210 -9.899 -0.031 12.005 44.366
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2008 2.2200 0.090 0.928
## Predictor 0.2417 0.2959 0.817 0.417
##
## Residual standard error: 17.36 on 61 degrees of freedom
## Multiple R-squared: 0.01082, Adjusted R-squared: -0.005394
## F-statistic: 0.6674 on 1 and 61 DF, p-value: 0.4171
##
##
## [[5]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.522 -9.357 -1.115 11.263 43.954
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.23107 2.25086 -0.103 0.919
## Predictor 0.04543 0.17809 0.255 0.799
##
## Residual standard error: 17.45 on 61 degrees of freedom
## Multiple R-squared: 0.001066, Adjusted R-squared: -0.01531
## F-statistic: 0.06508 on 1 and 61 DF, p-value: 0.7995
##
##
## [[6]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.773 -11.461 -1.726 12.033 44.086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1287 2.1895 -0.059 0.953
## Predictor 0.1040 0.1379 0.754 0.454
##
## Residual standard error: 17.38 on 61 degrees of freedom
## Multiple R-squared: 0.009234, Adjusted R-squared: -0.007008
## F-statistic: 0.5685 on 1 and 61 DF, p-value: 0.4537
##
##
## [[7]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.394 -9.848 -1.025 11.930 43.917
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.2071 2.2102 -0.094 0.926
## Predictor 0.1058 0.2609 0.405 0.687
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.002688, Adjusted R-squared: -0.01366
## F-statistic: 0.1644 on 1 and 61 DF, p-value: 0.6866
##
##
## [[8]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.680 -9.609 -2.472 12.374 39.303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.1762 2.2439 -0.524 0.6021
## Predictor -1.4279 0.8546 -1.671 0.0999 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.07 on 61 degrees of freedom
## Multiple R-squared: 0.04376, Adjusted R-squared: 0.02809
## F-statistic: 2.792 on 1 and 61 DF, p-value: 0.09988
##
##
## [[9]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.396 -10.343 0.704 10.899 43.290
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.3835 2.3008 -0.601 0.550
## Predictor -1.4685 0.9262 -1.586 0.118
##
## Residual standard error: 17.11 on 61 degrees of freedom
## Multiple R-squared: 0.03959, Adjusted R-squared: 0.02384
## F-statistic: 2.514 on 1 and 61 DF, p-value: 0.118
##
##
## [[10]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.598 -9.754 -1.585 11.719 43.459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.19548 2.46798 -0.079 0.937
## Predictor -0.07488 0.95599 -0.078 0.938
##
## Residual standard error: 17.46 on 61 degrees of freedom
## Multiple R-squared: 0.0001006, Adjusted R-squared: -0.01629
## F-statistic: 0.006135 on 1 and 61 DF, p-value: 0.9378
##
##
## [[11]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.940 -12.010 -0.993 11.476 42.381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.3056 2.1879 -0.140 0.889
## Predictor 0.4591 0.3754 1.223 0.226
##
## Residual standard error: 17.21 on 60 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.02432, Adjusted R-squared: 0.008063
## F-statistic: 1.496 on 1 and 60 DF, p-value: 0.2261
##
##
## [[12]]
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.09 -10.55 -0.76 11.15 44.31
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1412 2.1884 0.065 0.949
## Predictor 0.3489 0.3108 1.123 0.266
##
## Residual standard error: 17.28 on 61 degrees of freedom
## Multiple R-squared: 0.02024, Adjusted R-squared: 0.004183
## F-statistic: 1.26 on 1 and 61 DF, p-value: 0.266
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## [1] "There are 1 NA in the matrix X in Kelowna station"
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -70.023 -36.364 -5.873 29.224 111.165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 263.636 5.783 45.588 <2e-16 ***
## Predictor -1.361 0.595 -2.288 0.0256 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.2 on 61 degrees of freedom
## Multiple R-squared: 0.07902, Adjusted R-squared: 0.06393
## F-statistic: 5.234 on 1 and 61 DF, p-value: 0.02563
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.948 -32.174 -0.383 33.268 87.311
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 250.4780 6.3037 39.735 <2e-16 ***
## Predictor 1.0201 0.4361 2.339 0.0226 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.13 on 61 degrees of freedom
## Multiple R-squared: 0.08232, Adjusted R-squared: 0.06727
## F-statistic: 5.472 on 1 and 61 DF, p-value: 0.02262
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -84.20 -28.34 1.48 28.05 112.02
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 253.6918 5.7381 44.212 <2e-16 ***
## Predictor 0.7858 0.3530 2.226 0.0297 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 42.29 on 61 degrees of freedom
## Multiple R-squared: 0.07515, Adjusted R-squared: 0.05999
## F-statistic: 4.957 on 1 and 61 DF, p-value: 0.02969
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.229 -33.578 4.456 31.865 113.254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 255.8871 5.7324 44.639 <2e-16 ***
## Predictor 1.4104 0.9849 1.432 0.157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 43.26 on 61 degrees of freedom
## Multiple R-squared: 0.03252, Adjusted R-squared: 0.01666
## F-statistic: 2.051 on 1 and 61 DF, p-value: 0.1572
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.644 -6.370 -1.502 7.853 44.049
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.01724 2.28755 -0.008 0.994
## Predictor -0.03184 0.23244 -0.137 0.892
##
## Residual standard error: 16.36 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.000318, Adjusted R-squared: -0.01663
## F-statistic: 0.01877 on 1 and 59 DF, p-value: 0.8915
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.090 -7.713 -1.469 7.909 42.323
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1801 2.4422 0.483 0.631
## Predictor -0.1778 0.1728 -1.029 0.308
##
## Residual standard error: 16.22 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01763, Adjusted R-squared: 0.0009797
## F-statistic: 1.059 on 1 and 59 DF, p-value: 0.3077
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.676 -7.304 -0.743 8.442 44.729
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.56134 2.24189 -0.250 0.803
## Predictor 0.07037 0.13652 0.515 0.608
##
## Residual standard error: 16.32 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.004483, Adjusted R-squared: -0.01239
## F-statistic: 0.2657 on 1 and 59 DF, p-value: 0.6082
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.661 -8.028 -1.185 8.051 43.888
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5305 2.1953 -0.242 0.810
## Predictor 0.2133 0.3720 0.573 0.568
##
## Residual standard error: 16.31 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.005543, Adjusted R-squared: -0.01131
## F-statistic: 0.3289 on 1 and 59 DF, p-value: 0.5685
## $Winter
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.860 -8.733 -1.768 12.110 43.269
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2931 2.3887 0.123 0.903
## Predictor -0.1049 0.2458 -0.427 0.671
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.002976, Adjusted R-squared: -0.01337
## F-statistic: 0.1821 on 1 and 61 DF, p-value: 0.6711
##
##
## $Spring
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -50.689 -10.185 -1.982 11.750 42.587
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61273 2.60676 0.235 0.815
## Predictor -0.09239 0.18034 -0.512 0.610
##
## Residual standard error: 17.42 on 61 degrees of freedom
## Multiple R-squared: 0.004284, Adjusted R-squared: -0.01204
## F-statistic: 0.2625 on 1 and 61 DF, p-value: 0.6103
##
##
## $Summer
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -51.392 -10.019 -1.273 11.883 44.143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.52160 2.36443 -0.221 0.826
## Predictor 0.06858 0.14544 0.472 0.639
##
## Residual standard error: 17.43 on 61 degrees of freedom
## Multiple R-squared: 0.003632, Adjusted R-squared: -0.0127
## F-statistic: 0.2224 on 1 and 61 DF, p-value: 0.6389
##
##
## $Fall
##
## Call:
## lm(formula = y ~ Predictor, data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.539 -11.274 -1.793 11.968 43.258
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.5690 2.3058 -0.247 0.806
## Predictor 0.2555 0.3962 0.645 0.521
##
## Residual standard error: 17.4 on 61 degrees of freedom
## Multiple R-squared: 0.006771, Adjusted R-squared: -0.009512
## F-statistic: 0.4158 on 1 and 61 DF, p-value: 0.5214
